About the role
What we're looking for?
We are seeking an experienced ML Infrastructure/Systems Engineer with at least 2 years of full-time experience in building and maintaining production-level ML systems. You should be comfortable designing scalable infrastructure from scratch, making informed design decisions by comparing various technologies, and have a track record of optimizing systems for latency, throughput, and cost. We're looking for someone with a broad understanding of the ML infra space, including inference infrastructure, real-time video streaming, and data engineering, who can own complex projects and debug distributed systems.
What you'll do:
- Own and build the serving stack for our multimodal AI workloads, optimizing for latency, throughput, and cost.
- Architect and manage systems for real-time, long-lived WebRTC connections to ensure smooth video and audio delivery.
- Build and orchestrate robust data pipelines for large-scale offline processing, evaluation, and training using frameworks like Dagster or Ray.
- Configure, maintain, and optimize our GPU clusters using Kubernetes and Terraform.
- Develop CI/CD, evaluation, and versioning systems to enable safe, zero-downtime model deployments and rapid iteration.
- Work closely with ML researchers and product engineers to build the foundational infrastructure that powers our visual conversational AI.